Niche inheritance: a cooperative pathway to enhance cancer cell fitness though ecosystem engineering

Cancer cells can be described as an invasive species that is able to establish itself in a new environment. The concept of niche construction can be utilized to describe the process by which cancer cells terraform their environment, thereby engineering an ecosystem that promotes the genetic fitness of the species. Ecological dispersion theory can then be utilized to describe and model the steps and barriers involved in a successful diaspora as the cancer cells leave the original host organ and migrate to new host organs to successfully establish a new metastatic community. These ecological concepts can be further utilized to define new diagnostic and therapeutic areas for lethal cancers.Comment: 8 pages, 1 Table, 4 Figure

Boger fluid flow through hyperbolic contraction microchannels

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Boger fluids are characterized by their constant viscosity and elasticity and are very useful to study pure elastic flow behavior. In this paper we assess the potential of a microfluidic hyperbolic contraction as a device to measure the relaxation time of low viscosity polymer solutions, which are difficult to characterize in a conventional capillary break-up extensional rheometer. For this purpose we initially characterize the shear and extensional rheology of aqueous solutions of polyacrylamide (PAA) at different concentrations (400, 250, 125 and 50 ppm) with 1% (w/w) of NaCl, which result in low viscosity Boger fluids. Subsequently, flow visualizations of their flow through a microfluidic hyperbolic contraction were carried out in order to quantify the relation between their degree of elasticity and the vortex growth upstream of the microchannel.Fundação para a Ciência e a Tecnologia (FCT), COMPETE and FEDER through projects PTDC/ EQU-FTT/ 71800/2006, PTDC/EQUFTT/ 70727/2006, PTDC/EME-MFE/099109/ 2008, REEQ/928/EME/2005 and REEQ/298/EME/2005

Running Genetic Algorithms in the Edge: A First Analysis

Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), TIN2017-88213-R (http://6city.lcc.uma.es), the Ministry of Education of Spain (FPU16/02595

Regression of murine lung tumors by the let-7 microRNA.

MicroRNAs (miRNAs) have recently emerged as an important new class of cellular regulators that control various cellular processes and are implicated in human diseases, including cancer. Here, we show that loss of let-7 function enhances lung tumor formation in vivo, strongly supporting the hypothesis that let-7 is a tumor suppressor. Moreover, we report that exogenous delivery of let-7 to established tumors in mouse models of non-small-cell lung cancer (NSCLC) significantly reduces the tumor burden. These results demonstrate the therapeutic potential of let-7 in NSCLC and point to miRNA replacement therapy as a promising approach in cancer treatment

45S5 bioglass-derived glass-ceramic scaffolds containing niobium obtained by gelcasting method

Scaffolds of bioglass derived from BG45S5 (45 wt% SiO2, 24.5 wt% CaO, 24.5 wt% Na2O and 6 wt% P2O5) containing 10 wt% niobium were prepared by gelcasting method. The scaffolds presented a 3D porous structure with interconnected and spherical pores (pore size range 100 µm to 500 µm) and high porosity (89%), similar to trabecular architecture of spongy bone. The compressive strength was 0.18 ± 0.03 MPa which is acceptable for bone repair applications. The in vitro biological studies showed cytocompatibility for human osteoblastic cells as well tendency for higher alkaline phosphatase activity. Therefore, the findings here suggest the great potential of the scaffolds for using in bone tissue engineering.This work was supported by São Paulo Research Foundation - FAPESP (Grant: 2015-24659-7), National Council for Scientific and Technological Development (Grant: 456461/2014-0) and Erasmus Mundus Program (Be Mundus Project). The authors acknowledge the use of the analytical instrumentation facility at I3S-Instituto de Investigação e Inovação em Saúde (Portugal) and the provision of Nb2O5 by CBMM - Companhia Brasileira de Metalurgia e Mineração

Microchannels analogues for the study of viscoelastic fluid flows through porous media

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.This work studies the flow behavior and related pressure losses of viscoelastic polymer solutions in microchannels with two different sequences of contraction/expansion, disposed in a symmetric and an asymmetric arrangement, respectively. These microfluidic devices are proposed as simplified microchannel analogues for the flow of Newtonian and viscoelastic fluids through porous media. The results show that the symmetric configuration mimics the pressure gradient of these polymer solutions through a porous medium at low flow rates (below a critical Deborah number, Decr), while the asymmetric arrangement gives the asymptotic limit at high De values (above Decr) as a consequence of the intrinsic differences in the extensional rate profiles defined by each microgeometry.Fundação para a Ciência e a Tecnologia (FCT), COMPETE and FEDER through projects PTDC/ EQU-FTT/ 71800/ 2006, PTDC/EQUFTT/ 70727/ 2006, PTDC/ EME-MFE/ 99109/ 2008 and REEQ/ 262/ EME/ 2005

A probability density function generator based on neural networks

© 2019 Elsevier B.V. In order to generate a probability density function (PDF) for fitting the probability distributions of practical data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be utilised as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of the trained deep learning model can be used to estimate the PDF. Numerical experiments with single and mixed distributions are conducted to evaluate the performance of the proposed method. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method